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Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

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Showing 6601–6650 of 17610 papers

TitleStatusHype
Baseline Models for Pronoun Prediction and Pronoun-Aware Translation—0
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents—0
Batch Normalized Recurrent Neural Networks—0
BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction—0
BAT: Learning to Reason about Spatial Sounds with Large Language Models—0
BayesFormer: Transformer with Uncertainty Estimation—0
Bayesian Language Model based on Mixture of Segmental Contexts for Spontaneous Utterances with Unexpected Words—0
Bayesian Language Modelling of German Compounds—0
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation—0
Bayesian Reward Models for LLM Alignment—0
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction—0
BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models—0
β-calibration of Language Model Confidence Scores for Generative QA—0
BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge—0
BDetCLIP: Multimodal Prompting Contrastive Test-Time Backdoor Detection—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
BEAF: Observing BEfore-AFter Changes to Evaluate Hallucination in Vision-language Models—0
Beam-Guided Knowledge Replay for Knowledge-Rich Image Captioning using Vision-Language Model—0
Beam Search for Solving Substitution Ciphers—0
Allies: Prompting Large Language Model with Beam Search—0
Beam Search with Bidirectional Strategies for Neural Response Generation—0
BeamSeg: A Joint Model for Multi-Document Segmentation and Topic Identification—0
BeanCounter: A low-toxicity, large-scale, and open dataset of business-oriented text—0
Beauty Before Age? Applying Subjectivity to Automatic English Adjective Ordering—0
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder—0
Behavior Gated Language Models—0
Behavior of Modern Pre-trained Language Models Using the Example of Probing Tasks—0
Behind the Scenes of an Evolving Event Cloze Test—0
belabBERT: a Dutch RoBERTa-based language model applied to psychiatric classification—0
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief—0
BenchDirect: A Directed Language Model for Compiler Benchmarks—0
BenchmarkCards: Large Language Model and Risk Reporting—0
Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT—0
Benchmarking Automatic Speech Recognition coupled LLM Modules for Medical Diagnostics—0
Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model—0
Benchmarking Foundation Models with Language-Model-as-an-Examiner—0
Benchmarking General-Purpose In-Context Learning—0
Benchmarking Harmonized Tariff Schedule Classification Models—0
Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correction—0
Benchmarking Large Language Model Capabilities for Conditional Generation—0
Benchmarking Large Language Models with Integer Sequence Generation Tasks—0
Benchmarking Large Language Model Volatility—0
Benchmarking LLM for Code Smells Detection: OpenAI GPT-4.0 vs DeepSeek-V3—0
Benchmarking Middle-Trained Language Models for Neural Search—0
Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design—0
BenCzechMark : A Czech-centric Multitask and Multimetric Benchmark for Large Language Models with Duel Scoring Mechanism—0
Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks—0
Bengali to Assamese Statistical Machine Translation using Moses (Corpus Based)—0
bert2BERT: Towards Reusable Pretrained Language Models—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified